Neural Machine Unranking
Summary
This research addresses machine unlearning in neural IR (information retrieval, the technology that ranks search results), a process called neural machine unranking (NuMuR) that selectively removes data from AI systems for privacy compliance. The authors propose CoCoL (contrastive and consistent loss, a method with two complementary training objectives), which uses a contrastive loss to reduce relevance scores on forgotten data while preserving performance on shared data, plus a consistent loss to maintain accuracy on retained data, demonstrating effective data removal across multiple neural ranking models.
Solution / Mitigation
The proposed solution is CoCoL, a dual-objective framework comprising: 1) a contrastive loss that reduces relevance scores on forget sets while maintaining performance on entangled samples, and 2) a consistent loss that preserves accuracy on the retain set. According to the paper, CoCoL achieves substantial forgetting with minimal retention and generalization performance loss.
Classification
Original source: http://ieeexplore.ieee.org/document/11313626
First tracked: June 4, 2026 at 08:03 PM
Classified by LLM (prompt v3) · confidence: 85%